Abstract 13690: Diagnosis of Left Ventricular Hypertrophy on 12-leads Electrocardiogram by Historical Criterion and Machine Learning Models

Abstract

Introduction: Diagnostic criterion of left ventricular hypertrophy (LVH) on 12-leads electrocardiogram (ECG) were established. We verified them comparing with artificial intelligence (AI) method. Hypothesis: Machine learning on 12-leads ECG show higher diagnostic performance comparing with historical criterion. Methods: First, consecutive 60 patients with LVH were recruited, and one to one matching with age and sex to patients with normal cardiac function was performed. Finally, 120 patients (69.6 ± 12.6years, 38men per group) were enrolled. LVH was defined as at least one LV wall (septum, posterior wall, apex) showed thickness over 15mm on ultrasound echocardiography. No sinus rhythm, and wide QRS cases were excluded. Results: By logistic regression analysis, 77 significant predictors were extracted. Among historical criterion, Cornell voltage showed high accuracy (0.783) and area under receiver operating characteristics curve analysis (AUROC; 0.808). Conversely, among AI methods, light gradient boosting machine demonstrated higher accuracy (0.843) and random forest method higher AUROC (0.882). V2/V2 S-wave amplitude and I/V5 T-wave amplitude played essential roles to build the AI models. Conclusions: AI diagnosis on ECG for LVH showed powerful diagnostic performance comparing historical criterion.